We employed a lightweight segmentation model, called Efficient-UNet [1]}, for the segmentation task. The Efficient-UNet is a fully convolutional network based on an encoder-decoder design paradigm where the encoder branch is the B0 variant of Efficient-Net [2]}. Using this model with pre-trained weights from ImageNet as a backbone allows the overall model to benefit from transfer-learning, by extracting better feature representations and gaining higher domain generalizability. The Jaccard loss function [3]} is robust against the imbalanced population of positive and negative pixels in the segmentation dataset, and thus has been utilised to train the model.
